{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T14:13:35Z","timestamp":1763302415185,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533510","type":"print"},{"value":"9789819533527","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3352-7_39","type":"book-chapter","created":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T14:09:25Z","timestamp":1763302165000},"page":"465-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Detection to\u00a0Mitigation: Addressing Gender Bias in\u00a0Chinese Texts via\u00a0Efficient Tuning and\u00a0Voting-Based Rebalancing"],"prefix":"10.1007","author":[{"given":"Chengyan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiqiang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"39_CR1","doi-asserted-by":"crossref","unstructured":"Agbesi, V.K., Chen, W., Yussif, S.B., Ukwuoma, C.C., Gu, Y.H., Al-Antari, M.A.: Mutcelm: an optimal multi-textCNN-based ensemble learning for text classification. Heliyon 10(19) (2024)","DOI":"10.1016\/j.heliyon.2024.e38515"},{"key":"39_CR2","unstructured":"Banerjee, S., Lavie, A.: Meteor: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization, pp. 65\u201372 (2005)"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Blodgett, S.L., Barocas, S., Daum\u00e9\u00a0III, H., Wallach, H.: Language (technology) is power: a critical survey of \u201cbias\u201d in NLP. arXiv preprint arXiv:2005.14050 (2020)","DOI":"10.18653\/v1\/2020.acl-main.485"},{"key":"39_CR4","unstructured":"Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In: Advances in Neural Information Processing Systems 29 (2016)"},{"key":"39_CR5","unstructured":"Cang, Y., Yang, W., Sun, D., Ye, Z., Zheng, Z.: Albert-driven ensemble learning for medical text classification. J. Comput. Technol. Software 3(6) (2024)"},{"key":"39_CR6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","volume":"151","author":"W Chen","year":"2017","unstructured":"Chen, W., et al.: A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151, 147\u2013160 (2017)","journal-title":"CATENA"},{"key":"39_CR7","unstructured":"Chen, Y.: Convolutional neural network for sentence classification. Master\u2019s thesis, University of Waterloo (2015)"},{"key":"39_CR8","unstructured":"Chu, Y., et\u00a0al.: Qwen2-audio technical report. arXiv preprint arXiv:2407.10759 (2024)"},{"issue":"11","key":"39_CR9","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/s42256-019-0105-5","volume":"1","author":"MR Costa-Juss\u00e0","year":"2019","unstructured":"Costa-Juss\u00e0, M.R.: An analysis of gender bias studies in natural language processing. Nat. Mach. Intell. 1(11), 495\u2013496 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"issue":"16","key":"39_CR11","doi-asserted-by":"publisher","first-page":"E3635","DOI":"10.1073\/pnas.1720347115","volume":"115","author":"N Garg","year":"2018","unstructured":"Garg, N., Schiebinger, L., Jurafsky, D., Zou, J.: Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl. Acad. Sci. 115(16), E3635\u2013E3644 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Jin, Z., Jin, D., Mueller, J., Matthews, N., Santus, E.: IMAT: unsupervised text attribute transfer via iterative matching and translation. arXiv preprint arXiv:1901.11333 (2019)","DOI":"10.18653\/v1\/D19-1306"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., Jia, R., He, H., Liang, P.: Delete, retrieve, generate: a simple approach to sentiment and style transfer. arXiv preprint arXiv:1804.06437 (2018)","DOI":"10.18653\/v1\/N18-1169"},{"key":"39_CR14","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)"},{"key":"39_CR15","unstructured":"Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Q., Qin, J., Ye, W., Mou, H., He, Y., Wang, K.: Adaptive prompt routing for arbitrary text style transfer with pre-trained language models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a038, pp. 18689\u201318697 (2024)","DOI":"10.1609\/aaai.v38i17.29832"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: P-tuning v2: prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602 (2021)","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Lu, K., Mardziel, P., Wu, F., Amancharla, P., Datta, A.: Gender bias in neural natural language processing. In: Logic, Language, and Security: Essays Dedicated to Andre Scedrov on the Occasion of His 65th Birthday, pp. 189\u2013202 (2020)","DOI":"10.1007\/978-3-030-62077-6_14"},{"issue":"8","key":"39_CR19","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"LST Memory","year":"1997","unstructured":"Memory, L.S.T.: Sepp hochreiter and j\u00fcrgen schmidhuber. Neural Comput. 9(8), 1735 (1997)","journal-title":"Neural Comput."},{"key":"39_CR20","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"issue":"41","key":"39_CR21","doi-asserted-by":"publisher","first-page":"16474","DOI":"10.1073\/pnas.1211286109","volume":"109","author":"CA Moss-Racusin","year":"2012","unstructured":"Moss-Racusin, C.A., Dovidio, J.F., Brescoll, V.L., Graham, M.J., Handelsman, J.: Science faculty\u2019s subtle gender biases favor male students. Proc. Natl. Acad. Sci. 109(41), 16474\u201316479 (2012)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"39_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2016.06.005","volume":"62","author":"A Onan","year":"2016","unstructured":"Onan, A., Koruko\u011flu, S., Bulut, H.: A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Syst. Appl. 62, 1\u201316 (2016)","journal-title":"Expert Syst. Appl."},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311\u2013318 (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"39_CR24","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"140","key":"39_CR25","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Reif, E., Ippolito, D., Yuan, A., Coenen, A., Callison-Burch, C., Wei, J.: A recipe for arbitrary text style transfer with large language models. arXiv preprint arXiv:2109.03910 (2021)","DOI":"10.18653\/v1\/2022.acl-short.94"},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval, vol.\u00a039. Cambridge University Press, Cambridge (2008)","DOI":"10.1017\/CBO9780511809071"},{"key":"39_CR28","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"39_CR29","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, 30 (2017)"},{"key":"39_CR30","unstructured":"Wang, X., Aitchison, L., Rudolph, M.: Lora ensembles for large language model fine-tuning. arXiv preprint arXiv:2310.00035 (2023)"},{"key":"39_CR31","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1162\/tacl_a_00240","volume":"6","author":"K Webster","year":"2018","unstructured":"Webster, K., Recasens, M., Axelrod, V., Baldridge, J.: Mind the gap: a balanced corpus of gendered ambiguous pronouns. Trans. Assoc. Comput. Linguist. 6, 605\u2013617 (2018)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"39_CR32","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"39_CR33","doi-asserted-by":"publisher","unstructured":"Wu, C., Fang, W., Dai, F., Yin, H.: A model ensemble approach with LLM for Chinese text classification. In: Xu, H., et al. (eds.) CHIP 2023. CCIS, vol. 2080, pp. 214\u2013230. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-97-1717-0_20","DOI":"10.1007\/978-981-97-1717-0_20"},{"key":"39_CR34","doi-asserted-by":"crossref","unstructured":"Wu, C., Lin, Z., Fang, W., Huang, Y.: A medical diagnostic assistant based on LLM. In: China Health Information Processing Conference, pp. 135\u2013147. Springer (2023)","DOI":"10.1007\/978-981-97-1717-0_12"},{"key":"39_CR35","unstructured":"Yin, K., Liu, C., Mostafavi, A., Hu, X.: Crisissense-LLM: instruction fine-tuned large language model for multi-label social media text classification in disaster informatics. arXiv preprint arXiv:2406.15477 (2024)"},{"key":"39_CR36","unstructured":"Zhang, G., et al.: Corgi-pm: a Chinese corpus for gender bias probing and mitigation. arXiv preprint arXiv:2301.00395 (2023)"},{"key":"39_CR37","unstructured":"Zhang, G., et al.: Corgi-PM: a Chinese corpus for gender bias probing and mitigation (2023). https:\/\/arxiv.org\/abs\/2301.00395"},{"key":"39_CR38","unstructured":"Zhang, Z., et al.: Style transfer as unsupervised machine translation. arXiv preprint arXiv:1808.07894 (2018)"},{"key":"39_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457 (2017)","DOI":"10.18653\/v1\/D17-1323"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Gender bias in coreference resolution: Evaluation and debiasing methods. arXiv preprint arXiv:1804.06876 (2018)","DOI":"10.18653\/v1\/N18-2003"},{"key":"39_CR41","unstructured":"Zhou, C., Sun, C., Liu, Z., Lau, F.: A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630 (2015)"},{"key":"39_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, K., et\u00a0al.: Promptrobust: towards evaluating the robustness of large language models on adversarial prompts. In: Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis, pp. 57\u201368 (2023)","DOI":"10.1145\/3689217.3690621"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3352-7_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T14:09:31Z","timestamp":1763302171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3352-7_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"ISBN":["9789819533510","9789819533527"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3352-7_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]},"assertion":[{"value":"17 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2025\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}